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Activity Recognition Based on Semi-supervised Learning
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people
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Real-time closed-world tracking
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Spine versus Porcupine: A Study in Distributed Wearable Activity Recognition
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Using pervasive computing to deliver elder care
inferredcites →https://doi.org/10.1111/j.2517-6161.1977.tb01600.xopenalex-snapshot
Maximum Likelihood from Incomplete Data Via the EM Algorithm
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Combining labeled and unlabeled data with co-training
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Applying co-training methods to statistical parsing
inferredcites →https://doi.org/10.7551/mitpress/1120.003.0053openalex-snapshot
PAC Generalization Bounds for Co-training
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Personalized Sleep Monitoring Using Smartphones and Semi-supervised Learning
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Activity Recognition Using a Few Label Samples
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Building Personalized Activity Recognition Models with Scarce Labeled Data Based on Class Similarities
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Training Pool Selection for Semi-supervised Learning
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Multi-Classifier Adaptive Training: Specialising an Activity Recognition Classifier Using Semi-supervised Learning
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Comparing self-supervised learning techniques for wearable human activity recognition
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ProCAVIAR: Hybrid Data-Driven and Probabilistic Knowledge-Based Activity Recognition
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Heterogeneous multimodal sensors based activity recognition system
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Combining Deep Learning Techniques for Enhanced Human Activity Recognition: A Hybrid CNN-LSTM Fusion Approach
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Intensity Classification of the Image Quality Metric Series Using AdaBoost and Co-training Method
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Multi-graph Based Semi-supervised Learning for Activity Recognition
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A Survey on Ambient-Assisted Living Tools for Older Adults
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Classifier Personalization for Activity Recognition Using Wrist Accelerometers
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Using Intelligent Personal Annotations to Improve Human Activity Recognition for Movements in Natural Environments
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Unobtrusive and Wearable Systems for Automatic Dietary Monitoring
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Modèles statistiques avancés pour la reconnaissance de l’activité physique dans un environnement non contrôlé en utilisant un réseau d’objets connectés
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